import gradio as gr import spaces from clip_slider_pipeline import T5SliderFlux from diffusers import FluxPipeline import torch import time import numpy as np import cv2 from PIL import Image def process_controlnet_img(image): controlnet_img = np.array(image) controlnet_img = cv2.Canny(controlnet_img, 100, 200) controlnet_img = HWC3(controlnet_img) controlnet_img = Image.fromarray(controlnet_img) # load pipelines pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) #pipe.enable_model_cpu_offload() t5_slider = T5SliderFlux(pipe, device=torch.device("cuda")) # pipe_adapter = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash").to("cuda", torch.float16) # pipe_adapter.scheduler = EulerDiscreteScheduler.from_config(pipe_adapter.scheduler.config) # #pipe_adapter.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin") # # scale = 0.8 # # pipe_adapter.set_ip_adapter_scale(scale) # clip_slider_ip = CLIPSliderXL(sd_pipe=pipe_adapter, device=torch.device("cuda")) # controlnet = ControlNetModel.from_pretrained( # "xinsir/controlnet-canny-sdxl-1.0", # insert here your choice of controlnet # torch_dtype=torch.float16 # ) # vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) # pipe_controlnet = StableDiffusionXLControlNetPipeline.from_pretrained( # "sd-community/sdxl-flash", # controlnet=controlnet, # vae=vae, # torch_dtype=torch.float16, # ) # t5_slider_controlnet = T5SliderFlux(sd_pipe=pipe_controlnet,device=torch.device("cuda")) # clip_slider_inv = CLIPSliderXL_inv(sd_pipe=pipe_inv,device=torch.device("cuda")) @spaces.GPU(duration=120) def generate(slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type = None, img = None, controlnet_scale= None, ip_adapter_scale=None, ): start_time = time.time() # check if avg diff for directions need to be re-calculated print("slider_x", slider_x) print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2) if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]): avg_diff = t5_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations).to(torch.float16) x_concept_1, x_concept_2 = slider_x[0], slider_x[1] if not sorted(slider_y) == sorted([y_concept_1, y_concept_2]): avg_diff_2nd = t5_slider.find_latent_direction(slider_y[0], slider_y[1], num_iterations=iterations).to(torch.float16) y_concept_1, y_concept_2 = slider_y[0], slider_y[1] end_time = time.time() print(f"direction time: {end_time - start_time:.2f} ms") start_time = time.time() if img2img_type=="controlnet canny" and img is not None: control_img = process_controlnet_img(img) image = t5_slider.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff, avg_diff_2nd=avg_diff_2nd) elif img2img_type=="ip adapter" and img is not None: image = t5_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff, avg_diff_2nd=avg_diff_2nd) else: # text to image image = t5_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff, avg_diff_2nd=avg_diff_2nd) end_time = time.time() print(f"generation time: {end_time - start_time:.2f} ms") comma_concepts_x = ', '.join(slider_x) comma_concepts_y = ', '.join(slider_y) avg_diff_x = avg_diff.cpu() avg_diff_y = avg_diff_2nd.cpu() return gr.update(label=comma_concepts_x, interactive=True),gr.update(label=comma_concepts_y, interactive=True), x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, image @spaces.GPU def update_scales(x,y,prompt,seed, steps, guidance_scale, avg_diff_x, avg_diff_y, img2img_type = None, img = None, controlnet_scale= None, ip_adapter_scale=None,): avg_diff = avg_diff_x.cuda() avg_diff_2nd = avg_diff_y.cuda() if img2img_type=="controlnet canny" and img is not None: control_img = process_controlnet_img(img) image = t5_slider.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) elif img2img_type=="ip adapter" and img is not None: image = t5_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) else: image = t5_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) return image @spaces.GPU def update_x(x,y,prompt,seed, steps, avg_diff_x, avg_diff_y, img2img_type = None, img = None): avg_diff = avg_diff_x.cuda() avg_diff_2nd = avg_diff_y.cuda() image = t5_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) return image @spaces.GPU def update_y(x,y,prompt,seed, steps, avg_diff_x, avg_diff_y, img2img_type = None, img = None): avg_diff = avg_diff_x.cuda() avg_diff_2nd = avg_diff_y.cuda() image = t5_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) return image css = ''' #group { position: relative; width: 420px; height: 420px; margin-bottom: 20px; background-color: white } #x { position: absolute; bottom: 0; left: 25px; width: 400px; } #y { position: absolute; bottom: 20px; left: 67px; width: 400px; transform: rotate(-90deg); transform-origin: left bottom; } #image_out{position:absolute; width: 80%; right: 10px; top: 40px} ''' with gr.Blocks(css=css) as demo: x_concept_1 = gr.State("") x_concept_2 = gr.State("") y_concept_1 = gr.State("") y_concept_2 = gr.State("") avg_diff_x = gr.State() avg_diff_y = gr.State() with gr.Tab("text2image"): with gr.Row(): with gr.Column(): slider_x = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) slider_y = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) prompt = gr.Textbox(label="Prompt") submit = gr.Button("find directions") with gr.Column(): with gr.Group(elem_id="group"): x = gr.Slider(minimum=-30, value=0, maximum=30, elem_id="x", interactive=False) y = gr.Slider(minimum=-30, value=0, maximum=30, elem_id="y", interactive=False) output_image = gr.Image(elem_id="image_out") with gr.Row(): generate_butt = gr.Button("generate") with gr.Accordion(label="advanced options", open=False): iterations = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=400) steps = gr.Slider(label = "num inference steps", minimum=1, value=4, maximum=10) guidance_scale = gr.Slider( label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=5, ) seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) with gr.Tab(label="image2image"): with gr.Row(): with gr.Column(): image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512)) slider_x_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) slider_y_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) img2img_type = gr.Radio(["controlnet canny", "ip adapter"], label="", info="") prompt_a = gr.Textbox(label="Prompt") submit_a = gr.Button("Submit") with gr.Column(): with gr.Group(elem_id="group"): x_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False) y_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False) output_image_a = gr.Image(elem_id="image_out") with gr.Row(): generate_butt_a = gr.Button("generate") with gr.Accordion(label="advanced options", open=False): iterations_a = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=300) steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30) guidance_scale_a = gr.Slider( label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=5, ) controlnet_conditioning_scale = gr.Slider( label="controlnet conditioning scale", minimum=0.5, maximum=5.0, step=0.1, value=0.7, ) ip_adapter_scale = gr.Slider( label="ip adapter scale", minimum=0.5, maximum=5.0, step=0.1, value=0.8, ) seed_a = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) submit.click(fn=generate, inputs=[slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y,], outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image]) generate_butt.click(fn=update_scales, inputs=[x,y, prompt, seed, steps, guidance_scale, avg_diff_x, avg_diff_y], outputs=[output_image]) generate_butt_a.click(fn=update_scales, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, guidance_scale_a, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a]) submit_a.click(fn=generate, inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, guidance_scale_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image_a]) if __name__ == "__main__": demo.launch()